ABSTRACT There is a critical need to help define CKD and AKI disease subgroups and identify critical cells, pathways and targets for novel therapies. The overarching objective of this Central Hub application for the KPMP is to create an environment to promote scientific rigor, patient safety, and the successful interdisciplinary team science necessary to result in major advances in kidney disease research. In order to accomplish this, we have assembled a team with expertise in data coordinating center management, programming, biostatistics, biomedical informatics, and epidemiology, with over 25 years of experience coordinating highly successful large-scale longitudinal research studies. The overarching objective of the Central Hub of the KPMP is to create an environment to promote scientific rigor, patient safety, and the successful interdisciplinary team science necessary to result in major advances in kidney disease research. A key component of this model is the KPMP Data and biosample Coordinating Center (DCC), which will have primary responsibility for planning, facilitating, monitoring, and tracking data and specimen collection at the recruitment sites. The DCC must provide organizational, statistical, and programming expertise in multi-center studies including, but not limited to: (1) study design and protocol development; (2) study implementation and execution; (3) data and sample transmission; (4) quality control; (5) data and specimen tracking; (6) analysis of data from multiple sources; (7) communication facilitation. In order to accomplish this, we have assembled a DCC team with expertise in data coordinating center management, programming, biostatistics, biomedical informatics, and epidemiology, with over 25 years of experience coordinating highly successful large-scale longitudinal research studies. The DCC will act as a core component of the KPMP Central Hub and will provide interfaces for data submission and sample tracking. All activities will utilize informatics tools we support and govern for a wide array of challenges, include our UW REDCap instance that hosts more than 3,000 projects in a scalable and secure computing environment for custom electronic data capture. We will also leverage our deep experience and capacity in de-identification of clinical datasets and our use of named-entity recognition algorithms for annotation of unstructured text. Data will be made available to the Data Visualization Core (DVC) and the Administrative Core (AC) through self-service web interfaces, application program interfaces (APIs) and relational databases. We will work closely with both cores to ensure the highest data quality and timely delivery of datasets for deployment into the KPMP Tissue Atlas and visualization tools.